Browsing by Author "Keshava, A."

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    Solar Irradiance forecasting using Recurrent Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2022) Shekar, D.D.; Hiremath, A.C.; Keshava, A.; Vinatha Urundady, U.
    Solar irradiance being the chief constituent of the solar power extraction is dominated by the atmospheric conditions. Prediction of irradiance data is highly sought after in the field of forecasting and predictive maintenance. For this purpose various machine learning methods are being used to improve the accuracy of the forecasted value. This paper aims at prediction of solar irradiance using Recurrent Neural Networks (RNN) using Long Short Term Memory (LSTM) architecture. Using different combinations of input in the supervised learning method the accuracy for single as well as multiple time steps are determined. The results are shown in the form of evaluation metric as well as the forecasted values and actual value comparison. It is seen that for single time step prediction the LSTM RNN puts out highly accurate values but error for higher time steps prediction accumulates in a compounded manner. It is also observed that using time based models along with the inputs increases the accuracy of the forecasted values. © 2022 IEEE.
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    Support Vector Regression based Forecasting of Solar Irradiance
    (Institute of Electrical and Electronics Engineers Inc., 2022) Shimpi, A.V.; Chandrasekar, A.; Keshava, A.; Vinatha Urundady, U.
    PV power is being increasingly popular in terms of distributed energy source and derives its energy from irradiation of the sun. This irradiation differs demographically and needs to be accurately modelled for optimizing the dispatch of the source. Many methods are already in use to forecast the sun irradiation primarily based on Neural Networks and Machine learning techniques. In this paper, Support Vector based prediction is implemented and verified on a set of data. Support Vector Regressor (SVR) is a method of shifting the data points to a hyperplane and finding the correlation between the data samples. Different Kernel functions are used to define the hyperplane and their performance compared. Various combinations of input data is used to obtain the output from the regressor. Prediction metrics are used to determine the efficacy of the algorithm and based on the metrics the worst and best models for forecasting are presented. © 2022 IEEE.